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          <h2 class="post-title" itemprop="name headline">《机器学习实战》之朴素贝叶斯（3）过滤垃圾邮件</h2>
        

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<ul>
<li><strong>转载请注明作者和出处：<a href="http://blog.csdn.net/u011475210" target="_blank" rel="noopener">http://blog.csdn.net/u011475210</a></strong></li>
<li><strong>代码地址：<a href="https://github.com/WordZzzz/ML/tree/master/Ch04" target="_blank" rel="noopener">https://github.com/WordZzzz/ML/tree/master/Ch04</a></strong></li>
<li><strong>操作系统：WINDOWS 10</strong></li>
<li><strong>软件版本：python-3.6.2-amd64</strong></li>
<li><strong>编&emsp;&emsp;者：WordZzzz</strong></li>
</ul>
<hr>
<h2 id="前言："><a href="#前言：" class="headerlink" title="前言："></a>前言：</h2><p>&emsp;&emsp;使用朴素贝叶斯解决一些现实生活的问题时，需要先从文本内容得到字符串列表，然后生成词向量。下面这个例子中，我们将了解朴素贝叶斯的一个最著名的应用：电子邮件垃圾过滤。</p>
<p>示例：使用朴素贝叶斯对电子邮件进行分类</p>
<ul>
<li>收集数据：提供文本文件。</li>
<li>准备数据：将文本文件解析成词条向量。</li>
<li>分析数据：检查词条确保解析的正确性。</li>
<li>训练算法：使用我们之前建立的trainNB0()函数。</li>
<li>测试算法：使用calssifyNB()，并且构建一个新的测试函数来计算文档集的错误率。</li>
<li>使用算法：构建一个完整的程序对一组文档进行分类，将错分的文档输出。</li>
</ul>
<h2 id="准备数据：切分文本"><a href="#准备数据：切分文本" class="headerlink" title="准备数据：切分文本"></a>准备数据：切分文本</h2><p>&emsp;&emsp;先前的次向量都是我们预先给定的，这次将介绍如何从文本文档中构建自己的词列表。对于一个文本字符串，可以使用Python的string.split()方法将其切分。</p>
<p>string.split()的使用详解，请打开传送门：<a href="http://blog.csdn.net/u011475210/article/details/77925994" target="_blank" rel="noopener">http://blog.csdn.net/u011475210/article/details/77925994</a></p>
<p>代码实现：</p>
<figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br></pre></td><td class="code"><pre><span class="line"><span class="function"><span class="keyword">def</span> <span class="title">textParse</span><span class="params">(bigString)</span>:</span></span><br><span class="line">	<span class="string">"""</span></span><br><span class="line"><span class="string">	Function：	切分文本</span></span><br><span class="line"><span class="string"></span></span><br><span class="line"><span class="string">	Args：		bigString：输入字符串</span></span><br><span class="line"><span class="string"></span></span><br><span class="line"><span class="string">	Returns：	[*]：切分后的字符串列表</span></span><br><span class="line"><span class="string">	"""</span></span><br><span class="line">	<span class="keyword">import</span> re</span><br><span class="line">	<span class="comment">#利用正则表达式，来切分句子，其中分隔符是除单词、数字外的任意字符串</span></span><br><span class="line"></span><br><span class="line">	listOfTokens = re.split(<span class="string">r'\W*'</span>, bigString)</span><br><span class="line">	<span class="comment">#返回切分后的字符串列表</span></span><br><span class="line">	<span class="keyword">return</span> [tok.lower() <span class="keyword">for</span> tok <span class="keyword">in</span> listOfTokens <span class="keyword">if</span> len(tok) &gt; <span class="number">2</span>]</span><br></pre></td></tr></table></figure>
<p>&emsp;&emsp;Python中有一些内嵌的方法，可以将字符串全部转换成小写（.lower()）或者大写（.upper()），借助这些方法可以达到目的。程序的最后一行就是用的这种方法。同时，如果某些文件包含一些URL（<a href="http://docs.google.com/support/bin/answer.py?hl=en&amp;answer=66343），例如ham下的6.txt，那么切分文本时就会出现很多单词，如py、hl，很显然这些都是没用的，所以我们在程序最后一行只输出长度大于2的词条，好机智哦！" target="_blank" rel="noopener">http://docs.google.com/support/bin/answer.py?hl=en&amp;answer=66343），例如ham下的6.txt，那么切分文本时就会出现很多单词，如py、hl，很显然这些都是没用的，所以我们在程序最后一行只输出长度大于2的词条，好机智哦！</a></p>
<h2 id="测试算法：使用朴素贝叶斯进行交叉验证"><a href="#测试算法：使用朴素贝叶斯进行交叉验证" class="headerlink" title="测试算法：使用朴素贝叶斯进行交叉验证"></a>测试算法：使用朴素贝叶斯进行交叉验证</h2><p>&emsp;&emsp;下面我们将文本解析器集成到一个完整的分类器中。</p>
<p>代码实现：</p>
<figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br><span class="line">17</span><br><span class="line">18</span><br><span class="line">19</span><br><span class="line">20</span><br><span class="line">21</span><br><span class="line">22</span><br><span class="line">23</span><br><span class="line">24</span><br><span class="line">25</span><br><span class="line">26</span><br><span class="line">27</span><br><span class="line">28</span><br><span class="line">29</span><br><span class="line">30</span><br><span class="line">31</span><br><span class="line">32</span><br><span class="line">33</span><br><span class="line">34</span><br><span class="line">35</span><br><span class="line">36</span><br><span class="line">37</span><br><span class="line">38</span><br><span class="line">39</span><br><span class="line">40</span><br><span class="line">41</span><br><span class="line">42</span><br><span class="line">43</span><br><span class="line">44</span><br><span class="line">45</span><br><span class="line">46</span><br><span class="line">47</span><br><span class="line">48</span><br><span class="line">49</span><br><span class="line">50</span><br><span class="line">51</span><br><span class="line">52</span><br><span class="line">53</span><br><span class="line">54</span><br><span class="line">55</span><br><span class="line">56</span><br><span class="line">57</span><br><span class="line">58</span><br><span class="line">59</span><br><span class="line">60</span><br><span class="line">61</span><br><span class="line">62</span><br><span class="line">63</span><br><span class="line">64</span><br><span class="line">65</span><br><span class="line">66</span><br><span class="line">67</span><br><span class="line">68</span><br><span class="line">69</span><br></pre></td><td class="code"><pre><span class="line"><span class="function"><span class="keyword">def</span> <span class="title">spamTest</span><span class="params">()</span>:</span></span><br><span class="line">	<span class="string">"""</span></span><br><span class="line"><span class="string">	Function：	贝叶斯垃圾邮件分类器</span></span><br><span class="line"><span class="string"></span></span><br><span class="line"><span class="string">	Args：		无</span></span><br><span class="line"><span class="string"></span></span><br><span class="line"><span class="string">	Returns：	float(errorCount)/len(testSet)：错误率</span></span><br><span class="line"><span class="string">				vocabList：词汇表</span></span><br><span class="line"><span class="string">				fullText：文档中全部单词</span></span><br><span class="line"><span class="string">	"""</span></span><br><span class="line">	<span class="comment">#初始化数据列表</span></span><br><span class="line">	docList = []; classList = []; fullText = []</span><br><span class="line">	<span class="comment">#导入文本文件</span></span><br><span class="line">	<span class="keyword">for</span> i <span class="keyword">in</span> range(<span class="number">1</span>, <span class="number">26</span>):</span><br><span class="line">		<span class="comment">#切分文本</span></span><br><span class="line">		wordList = textParse(open(<span class="string">'email/spam/%d.txt'</span> % i).read())</span><br><span class="line">		<span class="comment">#切分后的文本以原始列表形式加入文档列表</span></span><br><span class="line">		docList.append(wordList)</span><br><span class="line">		<span class="comment">#切分后的文本直接合并到词汇列表</span></span><br><span class="line">		fullText.extend(wordList)</span><br><span class="line">		<span class="comment">#标签列表更新</span></span><br><span class="line">		classList.append(<span class="number">1</span>)</span><br><span class="line">		<span class="comment">#切分文本</span></span><br><span class="line">		<span class="comment">#print('i = :', i)</span></span><br><span class="line">		wordList = textParse(open(<span class="string">'email/ham/%d.txt'</span> % i).read())</span><br><span class="line">		<span class="comment">#切分后的文本以原始列表形式加入文档列表</span></span><br><span class="line">		docList.append(wordList)</span><br><span class="line">		<span class="comment">#切分后的文本直接合并到词汇列表</span></span><br><span class="line">		fullText.extend(wordList)</span><br><span class="line">		<span class="comment">#标签列表更新</span></span><br><span class="line">		classList.append(<span class="number">0</span>)</span><br><span class="line">	<span class="comment">#创建一个包含所有文档中出现的不重复词的列表</span></span><br><span class="line">	vocabList = createVocabList(docList)</span><br><span class="line">	<span class="comment">#初始化训练集和测试集列表</span></span><br><span class="line">	trainingSet = list(range(<span class="number">50</span>)); testSet = []</span><br><span class="line">	<span class="comment">#随机构建测试集，随机选取十个样本作为测试样本，并从训练样本中剔除</span></span><br><span class="line">	<span class="keyword">for</span> i <span class="keyword">in</span> range(<span class="number">10</span>):</span><br><span class="line">		<span class="comment">#随机得到Index</span></span><br><span class="line">		randIndex = int(random.uniform(<span class="number">0</span>, len(trainingSet)))</span><br><span class="line">		<span class="comment">#将该样本加入测试集中</span></span><br><span class="line">		testSet.append(trainingSet[randIndex])</span><br><span class="line">		<span class="comment">#同时将该样本从训练集中剔除</span></span><br><span class="line">		<span class="keyword">del</span>(trainingSet[randIndex])</span><br><span class="line">	<span class="comment">#初始化训练集数据列表和标签列表</span></span><br><span class="line">	trainMat = []; trainClasses = []</span><br><span class="line">	<span class="comment">#遍历训练集</span></span><br><span class="line">	<span class="keyword">for</span> docIndex <span class="keyword">in</span> trainingSet:</span><br><span class="line">		<span class="comment">#词表转换到向量，并加入到训练数据列表中</span></span><br><span class="line">		trainMat.append(setOfWords2Vec(vocabList, docList[docIndex]))</span><br><span class="line">		<span class="comment">#相应的标签也加入训练标签列表中</span></span><br><span class="line">		trainClasses.append(classList[docIndex])</span><br><span class="line">	<span class="comment">#朴素贝叶斯分类器训练函数</span></span><br><span class="line">	p0V, p1V, pSpam = trainNB0(array(trainMat), array(trainClasses))</span><br><span class="line">	<span class="comment">#初始化错误计数</span></span><br><span class="line">	errorCount = <span class="number">0</span></span><br><span class="line">	<span class="comment">#遍历测试集进行测试</span></span><br><span class="line">	<span class="keyword">for</span> docIndex <span class="keyword">in</span> testSet:</span><br><span class="line">		<span class="comment">#词表转换到向量</span></span><br><span class="line">		wordVector = setOfWords2Vec(vocabList, docList[docIndex])</span><br><span class="line">		<span class="comment">#判断分类结果与原标签是否一致</span></span><br><span class="line">		<span class="keyword">if</span> classifyNB(array(wordVector), p0V, p1V, pSpam) != classList[docIndex]:</span><br><span class="line">			<span class="comment">#如果不一致则错误计数加1</span></span><br><span class="line">			errorCount += <span class="number">1</span></span><br><span class="line">			<span class="comment">#并且输出出错的文档</span></span><br><span class="line">			print(<span class="string">"classification error"</span>,docList[docIndex])</span><br><span class="line">	<span class="comment">#打印输出信息</span></span><br><span class="line">	print(<span class="string">'the erroe rate is: '</span>, float(errorCount)/len(testSet))</span><br><span class="line">	<span class="comment">#返回词汇表和全部单词列表</span></span><br><span class="line">	<span class="comment">#return vocabList, fullText</span></span><br></pre></td></tr></table></figure>
<p>输出结果：</p>
<figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br></pre></td><td class="code"><pre><span class="line"><span class="meta">&gt;&gt;&gt; </span>bayes.spamTest()</span><br><span class="line">the erroe rate <span class="keyword">is</span>:  <span class="number">0.0</span></span><br><span class="line"><span class="meta">&gt;&gt;&gt; </span>bayes.spamTest()</span><br><span class="line">the erroe rate <span class="keyword">is</span>:  <span class="number">0.0</span></span><br><span class="line"><span class="meta">&gt;&gt;&gt; </span>bayes.spamTest()</span><br><span class="line">classification error [<span class="string">'home'</span>, <span class="string">'based'</span>, <span class="string">'business'</span>, <span class="string">'opportunity'</span>, <span class="string">'knocking'</span>, <span class="string">'your'</span>, <span class="string">'door'</span>, <span class="string">'don抰'</span>, <span class="string">'rude'</span>, <span class="string">'and'</span>, <span class="string">'let'</span>, <span class="string">'this'</span>, <span class="string">'chance'</span>, <span class="string">'you'</span>, <span class="string">'can'</span>, <span class="string">'earn'</span>, <span class="string">'great'</span>, <span class="string">'income'</span>, <span class="string">'and'</span>, <span class="string">'find'</span>, <span class="string">'your'</span>, <span class="string">'financial'</span>, <span class="string">'life'</span>, <span class="string">'transformed'</span>, <span class="string">'learn'</span>, <span class="string">'more'</span>, <span class="string">'here'</span>, <span class="string">'your'</span>, <span class="string">'success'</span>, <span class="string">'work'</span>, <span class="string">'from'</span>, <span class="string">'home'</span>, <span class="string">'finder'</span>, <span class="string">'experts'</span>]</span><br><span class="line">classification error [<span class="string">'scifinance'</span>, <span class="string">'now'</span>, <span class="string">'automatically'</span>, <span class="string">'generates'</span>, <span class="string">'gpu'</span>, <span class="string">'enabled'</span>, <span class="string">'pricing'</span>, <span class="string">'risk'</span>, <span class="string">'model'</span>, <span class="string">'source'</span>, <span class="string">'code'</span>, <span class="string">'that'</span>, <span class="string">'runs'</span>, <span class="string">'300x'</span>, <span class="string">'faster'</span>, <span class="string">'than'</span>, <span class="string">'serial'</span>, <span class="string">'code'</span>, <span class="string">'using'</span>, <span class="string">'new'</span>, <span class="string">'nvidia'</span>, <span class="string">'fermi'</span>, <span class="string">'class'</span>, <span class="string">'tesla'</span>, <span class="string">'series'</span>, <span class="string">'gpu'</span>, <span class="string">'scifinance'</span>, <span class="string">'derivatives'</span>, <span class="string">'pricing'</span>, <span class="string">'and'</span>, <span class="string">'risk'</span>, <span class="string">'model'</span>, <span class="string">'development'</span>, <span class="string">'tool'</span>, <span class="string">'that'</span>, <span class="string">'automatically'</span>, <span class="string">'generates'</span>, <span class="string">'and'</span>, <span class="string">'gpu'</span>, <span class="string">'enabled'</span>, <span class="string">'source'</span>, <span class="string">'code'</span>, <span class="string">'from'</span>, <span class="string">'concise'</span>, <span class="string">'high'</span>, <span class="string">'level'</span>, <span class="string">'model'</span>, <span class="string">'specifications'</span>, <span class="string">'parallel'</span>, <span class="string">'computing'</span>, <span class="string">'cuda'</span>, <span class="string">'programming'</span>, <span class="string">'expertise'</span>, <span class="string">'required'</span>, <span class="string">'scifinance'</span>, <span class="string">'automatic'</span>, <span class="string">'gpu'</span>, <span class="string">'enabled'</span>, <span class="string">'monte'</span>, <span class="string">'carlo'</span>, <span class="string">'pricing'</span>, <span class="string">'model'</span>, <span class="string">'source'</span>, <span class="string">'code'</span>, <span class="string">'generation'</span>, <span class="string">'capabilities'</span>, <span class="string">'have'</span>, <span class="string">'been'</span>, <span class="string">'significantly'</span>, <span class="string">'extended'</span>, <span class="string">'the'</span>, <span class="string">'latest'</span>, <span class="string">'release'</span>, <span class="string">'this'</span>, <span class="string">'includes'</span>]</span><br><span class="line">the erroe rate <span class="keyword">is</span>:  <span class="number">0.2</span></span><br></pre></td></tr></table></figure>
<p>&emsp;&emsp;函数spamTest()会输出在10封随机选择的电子邮件上的分类错误率。因为是随机的，所以每次输出结果可能有些差别。所以如果想要更好的估计错误率，就需要多次重复求平均值。</p>
<h2 id="报错信息汇总"><a href="#报错信息汇总" class="headerlink" title="报错信息汇总"></a>报错信息汇总</h2><p>运行报错：<br><figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br></pre></td><td class="code"><pre><span class="line"><span class="meta">&gt;&gt;&gt; </span>reload(bayes)</span><br><span class="line">&lt;module <span class="string">'bayes'</span> <span class="keyword">from</span> <span class="string">'E:\\机器学习实战\\mycode\\Ch04\\bayes.py'</span>&gt;</span><br><span class="line"><span class="meta">&gt;&gt;&gt; </span>bayes.spamTest()</span><br><span class="line">Traceback (most recent call last):</span><br><span class="line">  File <span class="string">"&lt;stdin&gt;"</span>, line <span class="number">1</span>, <span class="keyword">in</span> &lt;module&gt;</span><br><span class="line">  File <span class="string">"E:\机器学习实战\mycode\Ch04\bayes.py"</span>, line <span class="number">221</span>, <span class="keyword">in</span> spamTest</span><br><span class="line">    wordList = textParse(open(<span class="string">'email/ham/%d.txt'</span> % i).read())</span><br><span class="line">UnicodeDecodeError: <span class="string">'gbk'</span> codec can<span class="string">'t decode byte 0xae in position 199: illegal multibyte sequence</span></span><br></pre></td></tr></table></figure></p>
<p>&emsp;&emsp;一看就是编码问题，所以在程序中加入了打印信息，想看看是哪个文档读取出了问题，最后发现数据集ham下第23个文本中有不能识别的字符（®），修改之后程序运转正常。如果从我的github上下载的数据集，那就大可放心，不会出现这种问题的。</p>
<p>报错文档：<br><figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br></pre></td><td class="code"><pre><span class="line">SciFinance now automatically generates GPU-enabled pricing &amp; risk model source code that runs up to <span class="number">50</span><span class="number">-300</span>x faster than serial code using a new NVIDIA Fermi-<span class="class"><span class="keyword">class</span> <span class="title">Tesla</span> 20-<span class="title">Series</span> <span class="title">GPU</span>.</span></span><br><span class="line"><span class="class"></span></span><br><span class="line">SciFinance® is a derivatives pricing and risk model development tool that automatically generates C/C++ and GPU-enabled source code from concise, high-level model specifications. No parallel computing or CUDA programming expertise is required.</span><br><span class="line"></span><br><span class="line">SciFinance<span class="string">'s automatic, GPU-enabled Monte Carlo pricing model source code generation capabilities have been significantly extended in the latest release. This includes:</span></span><br></pre></td></tr></table></figure></p>
<p>运行报错：</p>
<figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br></pre></td><td class="code"><pre><span class="line"><span class="meta">&gt;&gt;&gt; </span>reload(bayes)</span><br><span class="line">&lt;module <span class="string">'bayes'</span> <span class="keyword">from</span> <span class="string">'E:\\机器学习实战\\mycode\\Ch04\\bayes.py'</span>&gt;</span><br><span class="line"><span class="meta">&gt;&gt;&gt; </span>bayes.spamTest()</span><br><span class="line">Traceback (most recent call last):</span><br><span class="line">  File <span class="string">"&lt;stdin&gt;"</span>, line <span class="number">1</span>, <span class="keyword">in</span> &lt;module&gt;</span><br><span class="line">  File <span class="string">"E:\机器学习实战\mycode\Ch04\bayes.py"</span>, line <span class="number">239</span>, <span class="keyword">in</span> spamTest</span><br><span class="line">    <span class="keyword">del</span>(trainingSet[randIndex])</span><br><span class="line">TypeError: <span class="string">'range'</span> object doesn<span class="string">'t support item deletion</span></span><br></pre></td></tr></table></figure>
<p>&emsp;&emsp;range()函数报错，这里主要涉及到python版本问题，详情请打开传送门：<a href="http://blog.csdn.net/u011475210/article/details/77925697" target="_blank" rel="noopener">http://blog.csdn.net/u011475210/article/details/77925697</a></p>
<p><strong><font color="red" size="3" face="仿宋">系列教程持续发布中，欢迎订阅、关注、收藏、评论、点赞哦～～(￣▽￣～)～</font></strong></p>
<p><strong><font color="red" size="3" face="仿宋">完的汪(∪｡∪)｡｡｡zzz</font></strong></p>

      
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